import os
import sys
import importlib
import json
import uuid
import numpy as np
import pandas as pd
import scipy.ndimage as snd
import skimage
import cv2
from matplotlib import pyplot as plt
import matplotlib as mpl
import plotly.express as px
import plotly.graph_objects as go
import plotly
if os.getcwd().split("/")[-1] == "notebooks": # if cwd is located where this file is
os.chdir("../..") # go two folders upward (the if statement prevents error if cell is rerun)
directory_path = os.path.abspath(os.path.join("src"))
if directory_path not in sys.path:
sys.path.append(directory_path)
all_metrics = pd.read_pickle("data/03_first_25percent_metrics/color_and_spatial_metrics_p4" + ".pkl")
all_metrics_flat = pd.read_pickle("data/03_first_25percent_metrics/color_and_spatial_metrics_flat_p4" + ".pkl")
all_metrics_agg = pd.read_pickle("data/03_first_25percent_metrics/color_and_spatial_metrics_agg_p4" + ".pkl")
kmeans_labels = pd.read_excel("data/01_raw/Ergonautus/Ergonautus_Clusters_Correct_Values.xlsx", dtype={
"Correct 1":"Int64", # "Int64" is from pandas, unlike int64 and allows null
"Correct 2":"Int64",
"Correct 3":"Int64",
"Borderline":"Int64",
"Notes":str,
"Filename":str,
}, na_filter=False) # False na_filters make empty value for str column be "" instead of NaN
def get_kmeans_metrics(clusters, kmeans_masks):
centers = clusters["center"]
ranges = clusters["max"] - clusters["min"]
spatial_metrics_list = [EyeTraumaAnalysis.get_spatial_metrics(kmeans_mask) for kmeans_mask in kmeans_masks]
spatial_metrics_pd = pd.concat([pd.DataFrame({
"x": [spatial_metrics["x"]["mean"] for spatial_metrics in spatial_metrics_list],
"y": [spatial_metrics["y"]["mean"] for spatial_metrics in spatial_metrics_list],}),
pd.DataFrame({
"x": [spatial_metrics["x"]["sd"] for spatial_metrics in spatial_metrics_list],
"y": [spatial_metrics["y"]["sd"] for spatial_metrics in spatial_metrics_list],
})], axis=1, keys=["Mean","SD"])
area_fractions = pd.DataFrame([np.count_nonzero(kmeans_mask)/np.prod(kmeans_mask.shape) for kmeans_mask in
kmeans_masks], columns=pd.MultiIndex.from_tuples([("","")]))
color_metrics = pd.concat([centers, ranges], axis=1, keys=["Center","Range"])
all_metrics = pd.concat([color_metrics, spatial_metrics_pd, area_fractions], axis=1,
keys=["Color","Location","Area"])
all_metrics_ranks = np.argsort(all_metrics, axis=0) + 1
return pd.concat([all_metrics, all_metrics_ranks], axis=1, keys=["Values","Ranks"])
def return_unique_colors(img):
channels = 1 if len(img.shape)==2 else img.shape[2]
img_linear = img.reshape((-1,channels))
return np.unique(img_linear, axis=0)
def recreate_kmeans(img, res_img, colorspace=None):
"""
img is original image
res is the kmeans reconstruction
colorspace doesn't change the actual arrays, just the columns names for the pandas dataframe outputted
"""
channels = img.shape[-1]
if colorspace is None:
if channels==3:
colorspace = "HSV"
elif channels==4:
colorspace = "BGRA"
else:
colorspace = "X" * channels
centers = return_unique_colors(res_img)
# Sort centers by HSV "value" - aka sort by grayscale
if colorspace.upper() in ["HSV"]:
centers = centers[centers[:, 2].argsort()]
#centers_indices = np.argsort(centers, axis=0) # sorts each column separately
elif colorspace.upper() in ["RGB","RGBA","BGR","BGRA"]:
v = np.max(centers[:, :3], axis=1) # the :3 is to remove an alpha channel if it exists
centers = centers[v.argsort()]
kmeans_masks = np.array([np.all(res_img == centers[ind], axis=2) for ind in range(centers.shape[0])])
mins = np.array([np.min(img[kmeans_mask],axis=0) for kmeans_mask in kmeans_masks])
maxs = np.array([np.max(img[kmeans_mask],axis=0) for kmeans_mask in kmeans_masks])
centers = pd.DataFrame(centers, columns=list(colorspace)) # list(.) converts "HSV" to ["H","S","V"]
mins = pd.DataFrame(mins, columns=list(colorspace))
maxs = pd.DataFrame(maxs, columns=list(colorspace))
clusters = pd.concat([centers,mins,maxs], axis=1, keys=["center","min","max"])
clusters[("ct","#")] = np.sum(kmeans_masks, axis=(1,2))
clusters[("ct","%")] = clusters[("ct","#")]/np.sum(clusters[("ct","#")])
return centers, kmeans_masks, res_img, clusters
all_metrics_dict = {}
all_kmeans_masks_dict = {}
for ind, filename in enumerate(kmeans_labels["Filename"]):
filebase, exten = filename.rsplit(".",maxsplit=1)
img_bgr = skimage.io.imread(os.path.join("data/01_raw/",filename))
res_bgr = skimage.io.imread(os.path.join("data/02_kmeans/",f"{filebase}_kmeanscolor.{exten}"))
res_gry = skimage.io.imread(os.path.join("data/02_kmeans/",f"{filebase}_grayscale.{exten}"))
img_bgr = img_bgr[:,:,:3] # remove alpha channel if it exists
res_bgr = res_bgr[:,:,:3]
img_hsv = cv2.cvtColor(img_bgr,cv2.COLOR_BGR2HSV)
res_hsv = cv2.cvtColor(res_bgr,cv2.COLOR_BGR2HSV)
#centers, kmeans_masks, _, clusters = recreate_kmeans(img_bgr, res_bgr, colorspace="BGR")
centers, kmeans_masks, _, clusters = recreate_kmeans(img_bgr, res_bgr, colorspace="HSV")
metrics = get_kmeans_metrics(clusters, kmeans_masks)
all_metrics_dict[filename] = metrics
all_kmeans_masks_dict[filename] = kmeans_masks
all_metrics = pd.concat(all_metrics_dict)
all_metrics.loc[:, ("Labels","Value","","")] = "False"
all_metrics.loc[:, ("Labels","Correct","","")] = False
all_metrics.loc[:, ("Labels","Borderline","","")] = False
for ind, row in kmeans_labels.iterrows():
filename = row["Filename"]
correct_indices =row[["Correct 1","Correct 2","Correct 3"]] - 1 # subtract by 1 to go from 1-10 to 0-9
correct_indices = correct_indices[~pd.isnull(correct_indices)].to_numpy().astype(int)
file_maskrow_correct_indices = [(filename,correct_index) for correct_index in correct_indices]
all_metrics.loc[file_maskrow_correct_indices, ("Labels","Correct","","")]=True
all_metrics.loc[file_maskrow_correct_indices, ("Labels","Value","","")]="True"
borderline_indices = row[["Borderline"]] - 1
borderline_indices = borderline_indices[~pd.isnull(borderline_indices)].to_numpy().astype(int)
file_maskrow_borderline_indices = [(filename,borderline_index) for borderline_index in borderline_indices]
all_metrics.loc[file_maskrow_borderline_indices, ("Labels","Borderline","","")]=True
all_metrics.loc[file_maskrow_borderline_indices, ("Labels","Value","","")]="Maybe"
# Reorder
all_metrics = all_metrics[["Labels","Ranks","Values"]]
# Label the second index
all_metrics.index.names = [all_metrics.index.names[0], "Mask"]
all_metrics_agg = all_metrics.groupby([("Labels","Value")]).agg(["median"])[["Ranks","Values"]]
all_metrics_agg = all_metrics.groupby([("Labels","Value")]).agg(["min","median","max"])
all_metrics_agg.to_excel("outputs/kmeans-descriptive/aggregate.xlsx")
/Users/ryerrabelli/opt/anaconda3/envs/EyeTraumaAnalysis/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3135: PerformanceWarning: indexing past lexsort depth may impact performance. has_raised = await self.run_ast_nodes(code_ast.body, cell_name, /Users/ryerrabelli/opt/anaconda3/envs/EyeTraumaAnalysis/lib/python3.10/site-packages/pandas/core/groupby/generic.py:979: PerformanceWarning: indexing past lexsort depth may impact performance. result = op.agg()
all_metrics_flat = all_metrics.copy()
all_metrics_flat.columns = ["-".join(multi_col).rstrip("-") for multi_col in all_metrics.columns]
all_metrics_flat = all_metrics_flat.reset_index()
# Create tuples of values i.e. "...-HSV" instead of just "...-H"
current_columns = all_metrics_flat.columns.copy()
for col in current_columns:
suffix = "-H"
if col.endswith(suffix):
col_prefix = col[0:-len(suffix)] # get column name up to suffix
# create a column that has the values as a tuple
all_metrics_flat[col_prefix + "-HSV"] = list(zip(
all_metrics_flat[col_prefix+"-H"],
all_metrics_flat[col_prefix+"-S"],
all_metrics_flat[col_prefix+"-V"],
))
suffix = "-x"
if col.endswith(suffix):
col_prefix = col[0:-len(suffix)] # get column name up to suffix
# check if column is integers
if pd.api.types.is_integer_dtype(all_metrics_flat[col_prefix + suffix]):
# create a column that has the values as a tuple
all_metrics_flat[col_prefix + "-xy"] = list(zip(
all_metrics_flat[col_prefix+"-x"],
all_metrics_flat[col_prefix+"-y"],
))
else:
# create a column that formatted strings - easier to view if numbers are originally floats instead of
# integers
all_metrics_flat[col_prefix + "-xy"] = \
all_metrics_flat[col_prefix+"-x"].map("{:.1%}, ".format) + \
all_metrics_flat[col_prefix+"-y"].map("{:.1%}".format)
for ind in range(1,10+1):
all_metrics_flat[f"Ranks-Color-Center-H>={ind}"] = all_metrics_flat["Ranks-Color-Center-H"] >=ind
for ind in range(1,10+1):
all_metrics_flat[f"Ranks-Color-Center-S>={ind}"] = all_metrics_flat["Ranks-Color-Center-S"] >=ind
for ind in range(1,10+1):
all_metrics_flat[f"Ranks-Color-Center-V>={ind}"] = all_metrics_flat["Ranks-Color-Center-V"] >=ind
for val_cutoff in [100]:
all_metrics_flat[f"Values-Color-Center-H>={val_cutoff}"] = all_metrics_flat["Values-Color-Center-H"] >=val_cutoff
for val_cutoff in range(135,175+1,5):
all_metrics_flat[f"Values-Color-Center-S>={val_cutoff}"] = all_metrics_flat["Values-Color-Center-S"] >=val_cutoff
for val_cutoff in range( 75,150+1,5):
all_metrics_flat[f"Values-Color-Center-V>={val_cutoff}"] = all_metrics_flat["Values-Color-Center-V"] >=val_cutoff
# Get Hue in terms of 360 degrees
K=10
angle_per_K = 360/10
all_metrics_flat["Ranks-Color-Center-H360"] = all_metrics_flat["Ranks-Color-Center-H"] * angle_per_K \
+ np.random.rand(len(all_metrics_flat["Values-Color-Center-H"]))\
*angle_per_K*0.75-angle_per_K*0.75/2
all_metrics_flat["Values-Color-Center-H360"] = all_metrics_flat["Values-Color-Center-H"] * 360/256
all_metrics.to_pickle("data/03_first_25percent_metrics/color_and_spatial_metrics" + ".pkl")
all_metrics_flat.to_pickle("data/03_first_25percent_metrics/color_and_spatial_metrics_flat" + ".pkl")
all_metrics_agg.to_pickle("data/03_first_25percent_metrics/color_and_spatial_metrics_agg.pkl")
all_metrics.to_excel("data/03_first_25percent_metrics/color_and_spatial_metrics" + ".xlsx")
all_metrics_flat.to_excel("data/03_first_25percent_metrics/color_and_spatial_metrics_flat" + ".xlsx")
all_metrics_agg.to_excel("data/03_first_25percent_metrics/color_and_spatial_metrics_agg" + ".xlsx")
default_plotly_save_scale = 4
notebook_name = "kmeans-descriptive"
def get_path_to_save(
plot_props:dict=None, file_prefix="",
save_filename:str=None, save_in_subfolder:str=None, extension="jpg", dot=".", create_folder_if_necessary=True):
replace_characters = {
"$": "",
"\\frac":"",
"\\mathrm":"",
"\\left(":"(",
"\\right)":")",
"\\left[":"[",
"\\right]":"]",
"\\": "",
"/":"-",
"{": "(",
"}": ")",
"<":"",
">":"",
"?":"",
"_":"",
"^":"",
"*":"",
"!":"",
":":"-",
"|":"-",
".":"_",
}
# define save_filename based on plot_props
if save_filename is None:
save_filename = "unnamed"
save_path = ["outputs", notebook_name,]
if save_in_subfolder is not None:
if isinstance(save_in_subfolder, (list, tuple, set, np.ndarray) ):
save_path.append(**save_in_subfolder)
else: # should be a string then
save_path.append(save_in_subfolder)
save_path = os.path.join(*save_path)
if not os.path.exists(save_path) and create_folder_if_necessary:
os.makedirs(save_path)
return os.path.join(save_path, file_prefix+save_filename+dot+extension)
def save_plotly_figure(fig: plotly.graph_objs.Figure,
title: str,
animated=False,
scale=None,
save_in_subfolder:str=None,
extensions=None
):
if scale is None:
scale = default_plotly_save_scale
if extensions is None:
extensions = ["html"]
if not animated:
# options = ['png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', 'json']
extensions += ["png","pdf"]
for extension in extensions:
try:
if extension in ["htm","html"]:
#fig.update_layout(title=dict(visible=False))
fig.write_html( get_path_to_save(save_filename=title, save_in_subfolder=save_in_subfolder, extension=extension),
full_html=True, include_plotlyjs="directory" )
else:
#if extension == "png":
# fig.update_layout(title=dict(visible=False))
fig.write_image(get_path_to_save(save_filename=title, save_in_subfolder=save_in_subfolder, extension=extension), scale=scale)
except ValueError as exc:
import traceback
traceback.print_exception(exc)
def customize_figure(fig: plotly.graph_objs.Figure,
#width=640, height=360,
width=None, height=None,
color_axes=["x"],
) -> dict:
""" - for plotly figures only. """
if "x" in color_axes:
fig.update_layout(
xaxis=dict(
range=[0,255],
showgrid=True,
gridcolor="#DDD",
)
)
if "y" in color_axes:
fig.update_layout(
yaxis=dict(
range=[0,255],
showgrid=True,
gridcolor="#DDD",
)
)
fig.update_layout(
font=dict(
family="Arial",
size=16,
color="black",
),
xaxis=dict(
zeroline=True,
mirror="ticks",
showspikes=True, spikemode="across", spikethickness=2, spikedash="solid"
),
yaxis=dict(
zeroline=True,
mirror="ticks",
showspikes=True, spikemode="across", spikethickness=2, spikedash="solid"
),
legend=dict(
yanchor="bottom",
y=0.01,
xanchor="right",
x=0.99,
bordercolor="Black", #font_size=16,
borderwidth=2,
),
autosize=False,
)
fig.update_layout(
font=dict(
family="Arial",
size=16,
color="black",
),
title={
"y":1,
"x":0.5,
"xanchor": "center",
"yanchor": "top",
"font":dict(size=16)
},
margin=dict(l=20, r=20, t=20, b=20),
legend=dict(
title={"font_family": "Arial Black",},
#bgcolor="LightSteelBlue",
bordercolor="Black", #font_size=16,
borderwidth=2,
),
bargap=0.05, bargroupgap=0.0,
dragmode="drawopenpath",
newshape_line_color="cyan",
)
if width is not None:
fig.update_layout(width=width)
if height is not None:
fig.update_layout(height=height)
config = {
"toImageButtonOptions" : {
"format": "png", # one of png, svg, jpeg, webp
"filename": 'custom_image',
"scale": default_plotly_save_scale # Multiply title/legend/axis/canvas sizes by this factor
},
"modeBarButtonsToAdd": ["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"]
}
return config
def rotate_z(x, y, z, theta):
"""For 3d plotly plots. This is helpful for creating animations with automatic rotating.
https://community.plotly.com/t/how-to-animate-a-rotation-of-a-3d-plot/20974
"""
w = x+1j*y
return np.real(np.exp(1j*theta)*w), np.imag(np.exp(1j*theta)*w), z
color_discrete_map = {
"True": px.colors.qualitative.Plotly[2], # green
"Maybe": px.colors.qualitative.Plotly[0], # blue
"False": px.colors.qualitative.Plotly[1], # red
}
pattern_shape_map = {}
category_orders = {
"Labels-Value": ["False", "Maybe", "True"],
}
# This is only the start. It will be added to programmatically later
var_labels = {
"Labels-Value": "Conjunctiva cluster",
"Values-Color-Center-H": "Center H",
"Values-Color-Center-S": "Center S",
"Values-Color-Center-V": "Center V",
"Values-Color-Range-H": "Range H",
"Values-Color-Range-S": "Range S",
"Values-Color-Range-V": "Range V",
"Values-Location-Mean-x": "Mean x",
"Values-Location-Mean-y": "Mean y",
"Values-Location-SD-x": "SD x",
"Values-Location-SD-y": "SD y",
"Ranks-Color-Mean-V>4": "V Rank >4",
"Ranks-Color-Mean-V>5": "V Rank >5"
}
var_labels_copy = var_labels.copy()
suffixes = ["-H","-x"]
for var_label_key in var_labels_copy:
for suffix in suffixes:
if var_label_key.endswith(suffix):
sep = suffix[:1] # should be "-"
suffix_letter = suffix[1:] # should be "-H" or "-x"
# Get name up to suffix letter e.g. "Values-Color-Center-"
var_label_key_prefix = var_label_key[0:-len(suffix_letter)]
# Get all possible suffixes for the prefix i.e. "H", "S", "V"
suffix_letter_options = [var_label_key[len(var_label_key_prefix):] for var_label_key in var_labels_copy
if var_label_key.startswith(var_label_key_prefix)]
combined_suffix_letters = "".join(suffix_letter_options)
# Get combined value
var_label_val_prefix = var_labels[var_label_key_prefix + suffix_letter][:-len(suffix_letter)]
combined_var_label_key = var_label_key_prefix + combined_suffix_letters
combined_var_label_val = var_label_val_prefix + combined_suffix_letters
var_labels[combined_var_label_key] = combined_var_label_val
# Add labels for ranks
var_labels_copy = var_labels.copy()
for var_label_key in var_labels_copy:
if var_label_key.startswith("Values-"):
var_label_key_suffix = var_label_key.split("Values-",maxsplit=1)[-1]
var_labels[f"Ranks-{var_label_key_suffix}"] = var_labels[var_label_key] + " (Rank)"
#point_hover_data = ["Values-Color-Center-HSV","Ranks-Color-Center-HSV",
# "Values-Location-Mean-xy","Ranks-Location-Mean-xy",
# "Values-Location-SD-xy","Ranks-Location-SD-xy"]
point_hover_data = {
"Values-Color-Center-H": False,
"Values-Color-Center-S": False,
"Values-Color-Center-V": False,
"Ranks-Color-Center-H": False,
"Ranks-Color-Center-S": False,
"Ranks-Color-Center-V": False,
"Values-Color-Center-HSV":True,
"Ranks-Color-Center-HSV":True,
"Values-Location-Mean-xy":True,
"Ranks-Location-Mean-xy":True,
"Values-Location-SD-xy":True,
"Ranks-Location-SD-xy":True,
}
plotly_template = "plotly_dark" #"simple_white" #"plotly_dark"
title = "Center V box plot"
fig = px.box(all_metrics_flat, x="Labels-Value", y="Values-Color-Center-V", points="all",
boxmode="group", notched=True, width=500, height=300,
category_orders=category_orders, labels=var_labels, template=plotly_template,
title=title,
)
fig.update_traces(marker=dict(size=2, opacity=0.8))
config = customize_figure(fig, color_axes="y")
fig.show()
save_plotly_figure(fig, title)
title = "HSV scatterplot- H center vs V center"
fig = px.scatter(all_metrics_flat, x="Values-Color-Center-H", y="Values-Color-Center-V",
marginal_x="histogram", marginal_y="histogram",
hover_data=point_hover_data,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_traces(marker=dict(size=2, opacity=0.8), selector=dict(type="scattergl"))
fig.for_each_trace( lambda trace: trace.update(marker=dict(size=2, opacity=0.8)) if trace.type == "scattergl" else (), )
config = customize_figure(fig, color_axes="xy")
fig.update_layout(legend=dict(
orientation="v",
yanchor="top", y=1,
xanchor="right", x=1,
)
)
fig.show()
save_plotly_figure(fig, title)
title = "HSV scatter matrix- HSV center"
fig = px.scatter_matrix(all_metrics_flat,
dimensions=["Values-Color-Center-H", "Values-Color-Center-S", "Values-Color-Center-V",],
hover_data=point_hover_data,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_traces(marker=dict(size=2, opacity=0.8))
#config = customize_figure(fig, color_axes="xy")
fig.show()
#save_plotly_figure(fig, title)
title = "HSV scatter matrix- HSV center"
fig = px.scatter_matrix(all_metrics_flat,
dimensions=["Values-Color-Center-H", "Values-Color-Center-S", "Values-Color-Center-V",],
hover_data=point_hover_data,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template="plotly_dark")
fig.update_traces(marker=dict(size=2, opacity=0.8))
fig.show()
save_plotly_figure(fig, title)
title = "HSV 3D scatter- HSV center"
fig = px.scatter_3d(all_metrics_flat,
x="Values-Color-Center-H", y="Values-Color-Center-S",z="Values-Color-Center-V",
hover_data=point_hover_data,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.for_each_trace( lambda trace: trace.update(marker=dict(size=2, opacity=0.8),) if trace.mode == "markers" else (), )
config = customize_figure(fig, color_axes="xy")
fig.update_layout(
scene = dict(
xaxis = dict(
backgroundcolor="rgb(200, 200, 230)",
showgrid=True,
gridcolor="white",
showbackground=True,
zeroline=True,
zerolinecolor="black",
range=[0,255],
),
yaxis = dict(
backgroundcolor="rgb(230, 200,230)",
showgrid=True,
gridcolor="white",
showbackground=True,
zeroline=True,
zerolinecolor="black",
range=[0,255],
),
zaxis = dict(
backgroundcolor="rgb(230, 230,200)",
showgrid=True,
gridcolor="white",
showbackground=True,
zeroline=True,
zerolinecolor="black",
range=[0,255],
),
# by turning of x and y spikes, the spikes seem more natural instead of box-like
xaxis_showspikes=False,
yaxis_showspikes=False,
),
scene_aspectmode="cube"
)
save_plotly_figure(fig, title)
x_eye = -1.25
y_eye = 2
z_eye = 0.5
fig.update_layout(
scene_camera_eye=dict(x=x_eye, y=y_eye, z=z_eye),
updatemenus=[dict(type='buttons',
showactive=False,
y=1,
x=0.8,
xanchor='left',
yanchor='bottom',
pad=dict(t=0, r=0),
buttons=[dict(label='Play',
method='animate',
args=[None, dict(frame=dict(duration=5, redraw=True),
transition=dict(duration=0),
fromcurrent=True,
mode='immediate'
)]
)
]
)
]
)
frames=[]
for t in np.arange(0, 2*3.14159, 0.1):
xe, ye, ze = rotate_z(x_eye, y_eye, z_eye, -t)
eye = dict(x=xe, y=ye, z=ze)
camera = dict(eye=eye)
frames.append(go.Frame(layout=dict(scene_camera=camera)))
fig.frames=frames
fig.show()
fig = px.scatter_polar(all_metrics_flat, theta="Ranks-Color-Center-H360", r="Ranks-Color-Center-S",
hover_data=point_hover_data,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_traces(marker=dict(size=4, opacity=0.8))
fig.show()
title = "HSV polar scatter- H by S"
fig = px.scatter_polar(all_metrics_flat, theta="Values-Color-Center-H", r="Values-Color-Center-S",
hover_data=point_hover_data,
title=title,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_traces(marker=dict(size=2, opacity=0.8))
save_plotly_figure(fig, title)
fig.show()
title = "HSV polar scatter- H by V"
fig = px.scatter_polar(all_metrics_flat, theta="Values-Color-Center-H", r="Values-Color-Center-V",
hover_data=point_hover_data,
title=title,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_traces(marker=dict(size=2, opacity=0.8))
save_plotly_figure(fig, title)
fig.show()
title = "HSV histogram with box plot- H val"
fig = px.histogram(all_metrics_flat, x="Values-Color-Center-H", marginal="box", opacity=0.6,
barmode="group", histnorm="percent",
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
title = "HSV histogram with box plot- S val"
fig = px.histogram(all_metrics_flat, x="Values-Color-Center-S", marginal="box", opacity=0.6,
barmode="group", histnorm="percent",
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
title = "HSV histogram with box plot- V val"
fig = px.histogram(all_metrics_flat, x="Values-Color-Center-V", marginal="box", opacity=0.6,
barmode="group", histnorm="percent",
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
title = "HSV histogram with box plot- H rank"
fig = px.histogram(all_metrics_flat, x="Ranks-Color-Center-H", marginal="box", opacity=0.6,
barmode="group", histnorm="percent", width=500, height=300,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
fig.show()
title = "HSV histogram with box plot- S rank"
fig = px.histogram(all_metrics_flat, x="Ranks-Color-Center-S", marginal="box", opacity=0.6,
barmode="group", histnorm="percent", width=500, height=300,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
fig.show()
title = "HSV histogram with box plot- V rank"
fig = px.histogram(all_metrics_flat, x="Ranks-Color-Center-V", marginal="box", opacity=0.6,
barmode="group", histnorm="percent", width=500, height=300,
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
title = "Location scatter matrix"
fig = px.scatter_matrix(all_metrics_flat,
dimensions=["Values-Location-Mean-x", "Values-Location-Mean-y",
"Values-Location-SD-x", "Values-Location-SD-y"],
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_traces(marker=dict(size=2, opacity=0.8))
fig.show()
save_plotly_figure(fig,title)
all_metrics_flat2 = all_metrics_flat[all_metrics_flat["Ranks-Color-Center-V"] >= 4]
title = "Location histogram with box plot- Mean x"
fig = px.histogram(all_metrics_flat2, x="Values-Location-Mean-x", marginal="box", opacity=0.6,
barmode="group", histnorm="percent",
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
title = "Location histogram with box plot- Mean y"
fig = px.histogram(all_metrics_flat2, x="Values-Location-Mean-y", marginal="box", opacity=0.6,
barmode="group", histnorm="percent",
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
title = "Location histogram with box plot- SD x"
fig = px.histogram(all_metrics_flat2, x="Values-Location-SD-x", marginal="box", opacity=0.6,
barmode="group", histnorm="percent",
color="Labels-Value", color_discrete_map=color_discrete_map,
category_orders=category_orders, labels=var_labels, template=plotly_template)
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)
title = "Location histogram with box plot- SD y"
fig = px.histogram(all_metrics_flat2, x="Values-Location-SD-y", marginal="box", opacity=0.6,
barmode="group", histnorm="percent",
color="Labels-Value", category_orders=category_orders, labels=var_labels, template="plotly_dark")
fig.update_layout(bargap=0.04)
fig.update_layout(font=dict(family="Arial",size=16,), margin=dict(l=20, r=20, t=20, b=20))
fig.show()
save_plotly_figure(fig, title)